The U.S. Department of Energy prepares an annual Electric Power Report that includes information about energy production, sales, consumption of fossil fuels, environmental data, and other topics related to energy. Additionally, the U.S. Department of Commerce tracks state annual summary statistics which include GDP by state from years 1998-2023.
This project will explore if states with a higher GDP are more likely to release CO2 into the atmosphere than states with a lower GDP.
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## layoutemissions <- read_excel("emission_annual.xlsx")
stategdp <- read_excel("stategdp_summary.xlsx")emissions_v2 <- emissions %>%
filter(`Year` >=1998,
`Producer Type` == "Total Electric Power Industry",
`Energy Source` == "All Sources",
`State` != "US-TOTAL")stategdp_v2 <- stategdp %>%
select(-GeoFips) %>%
pivot_longer(!GeoName, names_to = "year", values_to = "gdp") %>%
filter(`GeoName` != "United States") %>%
mutate(`State`= state.abb[match(`GeoName`,state.name)]) %>%
select(-GeoName) %>%
drop_na()
stategdp_v2$year <- as.numeric(stategdp_v2$year)
stategdp_v3 <- stategdp_v2 %>%
rename("Year" = "year",
"GDP" = "gdp")state_gdp_emissions <- left_join(emissions_v2, stategdp_v3, by = c('State','Year'))
state_gdp_emissions <- state_gdp_emissions |>
drop_na() |>
rename("CO2" = "CO2\r\n(Metric Tons)")Are states with higher GDPs more likely to produce energy that emits CO2?
Each case represents a states annual energy usage and GDP for the
respective year. There are 1326 rows in the data set and eight columns:
1. Year
2. State
3. Producer Type
4. Energy Source
5. CO2 (Metric Tons)
6. SO2 (Metric Tons)
7. NOx
(Metric Tons)
8. GDP
This project uses data from the Department of Energy’s (DOE) Annual Electric Power Report and the U.S. Department of Commerce (DOC) State Annual Summary Statistics.
The Annual Electric Power Report is prepared by the Office of Energy Production, Conversion, and Delivery (EPCD), within the U.S. Energy Information Administration in DOE. Data in the report is provided directly by respondents into DOE’s information systems. For more information about the data collection process, please see the Data Quality and Submission section of the Electric Power Annual Report: eia.gov/electricity/annual/pdf/epa.pdf.
The State Annual Summary Statistics provided by DOC are estimated using two data sources: (1) wages and salaries data from the Bureau of Labor Statistics and (2) value-added, receipts, and payroll data from the Census Bureau’s economic censuses. These data sources are then used to estimate the State GDP following the estimation methodology outlined in the DOC’s Gross Domestric Product by State Estimation Methodology. For more information please see the methodology: bea.gov/sites/default/files/methodologies/0417_GDP_by_State_Methodology.pdf.
This is an observational study.
The response variable is state GDP and is numerical.
The explanatory variable is CO2 Emissions from Energy Production and is numerical.
describe(state_gdp_emissions)## state_gdp_emissions
##
## 8 Variables 1300 Observations
## --------------------------------------------------------------------------------
## Year
## n missing distinct Info Mean pMedian Gmd .05
## 1300 0 26 0.999 2010 2010 8.661 1999
## .10 .25 .50 .75 .90 .95
## 2000 2004 2010 2017 2021 2022
##
## lowest : 1998 1999 2000 2001 2002, highest: 2019 2020 2021 2022 2023
## --------------------------------------------------------------------------------
## State
## n missing distinct
## 1300 0 50
##
## lowest : AK AL AR AZ CA, highest: VT WA WI WV WY
## --------------------------------------------------------------------------------
## Producer Type
## n missing
## 1300 0
## distinct value
## 1 Total Electric Power Industry
##
## Value Total Electric Power Industry
## Frequency 1300
## Proportion 1
## --------------------------------------------------------------------------------
## Energy Source
## n missing distinct value
## 1300 0 1 All Sources
##
## Value All Sources
## Frequency 1300
## Proportion 1
## --------------------------------------------------------------------------------
## CO2
## n missing distinct Info Mean pMedian Gmd .05
## 1300 0 1300 1 43277808 37130691 41370454 2410656
## .10 .25 .50 .75 .90 .95
## 3610522 13537718 33019610 57791802 89115625 119709509
##
## lowest : 6583 6733 7098 8016 8209
## highest: 259415102 260213902 261332154 264816156 267464092
## --------------------------------------------------------------------------------
## SO2
## (Metric Tons)
## n missing distinct Info Mean pMedian Gmd .05
## 1300 0 1283 1 121374 65259 165607 703.6
## .10 .25 .50 .75 .90 .95
## 2653.7 11865.0 43995.0 125364.8 357329.4 558374.6
##
## lowest : 28 32 34 36 37
## highest: 1095719 1140670 1152407 1222226 1321325
## --------------------------------------------------------------------------------
## NOx
## (Metric Tons)
## n missing distinct Info Mean pMedian Gmd .05
## 1300 0 1293 1 60188 46308 64043 2270
## .10 .25 .50 .75 .90 .95
## 6041 16029 39792 77150 145981 203583
##
## lowest : 409 419 457 484 487, highest: 403364 483133 509777 510931 531361
## --------------------------------------------------------------------------------
## GDP
## n missing distinct Info Mean pMedian Gmd .05
## 1300 0 1300 1 321806 233110 356429 32552
## .10 .25 .50 .75 .90 .95
## 43450 74347 189134 389481 703112 1107879
##
## lowest : 14833.2 15685.7 16197.7 16842.7 17083.7
## highest: 3068630 3076760 3423960 3660420 3870380
## --------------------------------------------------------------------------------#Findings: DC may need to be excluded from this evaluation due to missing data.
gdp_plot<- ggplot(state_gdp_emissions, aes(x=Year, y=GDP, color=State))+
geom_line()+
labs(title ="State GDP Over Time",
x="Year",
y= "GDP (in billions)")+
theme(legend.position="none")
co2_plot <- ggplot(state_gdp_emissions, aes(x=Year, y=`CO2`, color=State))+
geom_line()+
labs(title ="State CO2 Emissions from Energy Production Over Time",
x="Year",
y= "CO2 Emissions (Metric Tons)")The graphs above are not very helpful if they are not interactive, therefore, I am providing the plots that are interactive and if you hover you can see summary information. Note, the interactive plots are only accessible in HTML files and within the Rmd.
ggplotly(gdp_plot)ggplotly(co2_plot)state_gdp_emissions |>
summarise(median_GDP = median(GDP),
mean_GDP = mean(GDP),
max_GDP = max(GDP),
iqr_GDP = IQR(GDP),
sd_GDP = sd(GDP)
)state_gdp_emissions |>
summarise(median_CO2 = median(CO2),
mean_CO2 = mean(CO2),
max_CO2 = max(CO2),
iqr_CO2 = IQR(CO2),
sd_CO2 = sd(CO2)
)length(unique(state_gdp_emissions$State))## [1] 50